Systems and methods for generating causal insight summary

ABSTRACT

Conventionally, text summarization has been rule-based method and neural network based which required large dataset for training and the summary delivered had to be assessed by user in terms of relevancy. System and method are provided by present disclosure that generate causal insight summaries wherein event of importance is detected, and it is determined why event is relevant to a user. Text description is processed for named entities recognition, polarities of sentences identified, extraction of causal effects sentences (CES) and causal relationship identification in text segments which correspond to impacting events. Named entities are then role labeled. A score is computed for named entities, polarities of sentences, causal effects sentences, causal relationships, and the impacting events. A causal insight summary is generated with overall polarity being computed/determined. A customized causal insight summary is delivered to target users based on user preferences associated with specific named entities and impacting events.

PRIORITY CLAIM

This U.S. patent application claims priority under 35 U.S.C. § 119 to:India Application No. 202121038709, filed on 26 Aug. 2021. The entirecontents of the aforementioned application are incorporated herein byreference.

TECHNICAL FIELD

The disclosure herein generally relates to text summarization, and, moreparticularly, to systems and methods for generating causal insightsummary.

BACKGROUND

Text summarization has attracted the attention of natural languageprocessing (NLP) researchers for a long time. Basically, there are twobroad kinds of summarization methods: extractive and abstractive.Extractive is a more guided or rule-based method in which key phrases orsubset of sentences of the original text are selected and create asummary. Some of the conventional early efforts concentrated on scoringwords and phrases based on their frequencies followed by sentencescoring methods. Another research work introduced Latent SemanticAnalysis (LSA) based approach which uses a singular value decompositionon word-sentence matrix. One of the most successful text summarizationsystems called TextRank was introduced in 2004 which uses a graph-basedalgorithm similar to PageRank research work, in which similarity betweentwo sentences is computed in terms of their content overlap. Later,enhanced TextRank was proposed in which there was a use of longestcommon substrings-based cosine distance between a pair of sentences. Inrecent years, machine learning methods have been used for domainspecific summarization such as scientific paper summarization,biographical summaries and biomedical summaries. Numerous supervisedapproaches like SummaRuNNer, SummCoder based on deep neural networkshave also been used for extractive summarization.

On the other hand, abstractive summarization is based on generating anew shorter text or new phrases that conveys the most criticalinformation from the original text. Several approaches have beenproposed for abstractive text summarization like semantic graph-basedapproach, sequence-to-sequence recurrent neural networks model, and viaphrase selection and merging them to form a meaningful sentence. Allthese approaches required a big dataset for training such as dailymail,CNN dataset, Gigaword dataset, and the like.

SUMMARY

Embodiments of the present disclosure present technological improvementsas solutions to one or more of the above-mentioned technical problemsrecognized by the inventors in conventional systems. For example, in oneaspect, there is provided a processor implemented method for generatinga causal insight summary. The method comprises obtaining, via one ormore hardware processors, a text description from one or more sources;pre-processing, via the one or more hardware processors, the textdescription to obtain pre-processed text description; identifying, byusing a named entity recognition technique via the one or more hardwareprocessors, one or more named entities from the pre-processed textdescription; performing a sentiment analysis, via the one or morehardware processors, on the pre-processed text description to identifyone or more polarities of sentences comprised in the pre-processed textdescription; extracting, via the one or more hardware processors, one ormore cause effects sentences in the pre-processed text description andidentifying one or more causal relationship between text segments in theone or more cause effects sentences, wherein the one or more causeeffects sentences correspond to one or more impacting events; assigning,via the one or more hardware processors, a role label to each of the oneor more named entities, wherein the role label corresponds to a role ofeach of the one or more entities in a corresponding event of the one ormore impacting events; computing, via the one or more hardwareprocessors, a score for one or more sentences in the text descriptionbased on a presence of (i) the one or more identified named entitiesbased on the role label assigned, (ii) the one or more identifiedpolarities, (iii) the one or more cause effects sentences, and (iv) theone or more impacting events; and generating, via the one or morehardware processors, the causal insight summary based on the computedscore.

In an embodiment, each of the one or more cause effects sentencescomprises an antecedent, a consequence, and a causal connector.

In an embodiment, interdependence of the one or more sentences is basedon the position of (i) each of the one or more identified namedentities, (ii) the one or more identified polarities, and (iii) the oneor more cause effects sentences in the pre-processed text description.

In an embodiment, the text segments comprise at least one of (i) acause, (ii) an effect, and (iii) an associated causal relationship.

In an embodiment, the method further comprises computing an overallpolarity of the generated causal insight summary.

In an embodiment, the method further comprises communicating at least aportion of the generated causal insight summary to one or more targetusers based on an interest of the one or more target users for at leastone of (i) one or more specific named entities, and (ii) the one or moreimpacting events.

In another aspect, there is provided a system for generating a causalinsight summary. The system comprises a memory storing instructions; oneor more communication interfaces; and one or more hardware processorscoupled to the memory via the one or more communication interfaces,wherein the one or more hardware processors are configured by theinstructions to: obtain a text description from one or more sources;pre-process the text description to obtain pre-processed textdescription; identify, by using a named entity recognition technique,one or more named entities from the pre-processed text description;perform a sentiment analysis on the pre-processed text description toidentify one or more polarities of sentences comprised in thepre-processed text description; extract one or more cause effectssentences in the pre-processed text description and identify one or morecausal relationship between text segments in the one or more causeeffects sentences, wherein the one or more cause effects sentencescorrespond to one or more impacting events; assign a role label to eachof the one or more named entities, wherein the role label corresponds toa role of each of the one or more entities in a corresponding event ofthe one or more impacting events; compute a score for one or moresentences in the text description based on a presence of (i) the one ormore identified named entities based on the role label assigned, (ii)the one or more identified polarities, (iii) the one or more causeeffects sentences, and (iv) the one or more impacting events; andgenerate the causal insight summary based on the computed score.

In an embodiment, each of the one or more cause effects sentencescomprises an antecedent, a consequence, and a causal connector.

In an embodiment, interdependence of the one or more sentences is basedon the position of (i) each of the one or more identified namedentities, (ii) the one or more identified polarities, and (iii) the oneor more cause effects sentences in the pre-processed text description.

In an embodiment, the text segments comprise at least one of (i) acause, (ii) an effect, and (iii) an associated causal relationship.

In an embodiment, the one or more hardware processors are furtherconfigured by the instructions to compute an overall polarity of thegenerated causal insight summary.

In an embodiment, the one or more hardware processors are furtherconfigured by the instructions to communicate at least a portion of thegenerated causal insight summary to one or more target users based on aninterest of the one or more target users for at least one of (i) one ormore specific named entities, and (ii) the one or more impacting events.

In yet another aspect, there are provided one or more non-transitorymachine-readable information storage mediums comprising one or moreinstructions which when executed by one or more hardware processorscause a method for generating causal insight summary. The methodcomprises obtaining, via the one or more hardware processors, a textdescription from one or more sources; pre-processing, via the one ormore hardware processors, the text description to obtain pre-processedtext description; identifying, by using a named entity recognitiontechnique via the one or more hardware processors, one or more namedentities from the pre-processed text description; performing a sentimentanalysis, via the one or more hardware processors, on the pre-processedtext description to identify one or more polarities of sentencescomprised in the pre-processed text description; extracting, via the oneor more hardware processors, one or more cause effects sentences in thepre-processed text description and identifying one or more causalrelationship between text segments in the one or more cause effectssentences, wherein the one or more cause effects sentences correspond toone or more impacting events; assigning, via the one or more hardwareprocessors, a role label to each of the one or more named entities,wherein the role label corresponds to a role of each of the one or moreentities in a corresponding event of the one or more impacting events;computing, via the one or more hardware processors, a score for one ormore sentences in the text description based on a presence of (i) theone or more identified named entities based on the role label assigned,(ii) the one or more identified polarities, (iii) the one or more causeeffects sentences, and (iv) the one or more impacting events; andgenerating, via the one or more hardware processors, the causal insightsummary based on the computed score.

In an embodiment, each of the one or more cause effects sentencescomprises an antecedent, a consequence, and a causal connector.

In an embodiment, interdependence of the one or more sentences is basedon the position of (i) each of the one or more identified namedentities, (ii) the one or more identified polarities, and (iii) the oneor more cause effects sentences in the pre-processed text description.

In an embodiment, the text segments comprise at least one of (i) acause, (ii) an effect, and (iii) an associated causal relationship.

In an embodiment, the method further comprises computing an overallpolarity of the generated causal insight summary.

In an embodiment, the method further comprises communicating at least aportion of the generated causal insight summary to one or more targetusers based on an interest of the one or more target users for at leastone of (i) one or more specific named entities, and (ii) the one or moreimpacting events.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate exemplary embodiments and, togetherwith the description, serve to explain the disclosed principles.

FIG. 1 depicts an exemplary system for generating causal insightsummary, in accordance with an embodiment of the present disclosure.

FIG. 2 depict an exemplary high level block diagram of the system ofFIG. 1 for generating causal insight summary, in accordance with anembodiment of the present disclosure.

FIG. 3 depicts an exemplary flow chart illustrating a method generatingcausal insight summary, using the system of FIG. 1 , in accordance withan embodiment of the present disclosure.

DETAILED DESCRIPTION

Exemplary embodiments are described with reference to the accompanyingdrawings. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears.Wherever convenient, the same reference numbers are used throughout thedrawings to refer to the same or like parts. While examples and featuresof disclosed principles are described herein, modifications,adaptations, and other implementations are possible without departingfrom the spirit and scope of the disclosed embodiments. It is intendedthat the following detailed description be considered as exemplary only,with the true scope and spirit being indicated by the following claims.

In this era of knowledge economy, all organizations are experiencing theneed to deliver relevant and timely actionable insights to theiremployees, customers, and partners about what is happening within anorganization or in the outside world that is explicitly or implicitlyrelated to them. These insights should be brief, crisp, andself-explanatory such that they can easily convey the key message to thereceiver without information overload. Delivering the right and relevantalerts to the right employee at the right time is essential for properfunctioning of a modern enterprise. On the other hand, getting real timealerts about financial entities, social disruptions, health, and safetyevents etc., are crucial for the conscious consumer today. Extractingand generating meaningful insights about relevant entities anddelivering them in a personalized and timely manner is a challengingtask.

An insight can be defined as a piece of knowledge derived from data ortext document that is causal in nature and acts as a trigger for afuture action. For example, insights about financial instruments can bederived from business news. Insights about future risks a company mightface can be derived from its actions reported in news in isolation or inconjunction with user reaction to these reports. Insights differ fromtext summaries in a big way that they specifically must be causal innature encapsulating elements of interest, incidents that might bereported to have impact on those elements of interest and/or also can beinferred based on additional external knowledge.

Though there exist several notification services that deliver tweets,targeted messages, News headlines or links to different kinds ofarticles, none of these systems provide insightful, self-explanatorysummaries automatically created from textual content. Thus, it is leftto the user to read, assimilate the information, and derive necessaryinsights from it by linking different information components that arerelevant to him or her. This is not only time-consuming but alsosubjective and potentially error prone. Since users usually subscribe tochannels and only have the option of applying some existing filters,they often end up receiving many more notifications than what isrelevant for them directly. It is not only wasteful to receive a wholelot of information that are not at all potentially relevant, but usersalso often end up unsubscribing from such channels due to theinformation overload.

Though text summarization is a well-researched problem of NaturalLanguage Processing, none of the summarization techniques focus ongenerating causal insights from large text. Further, no generic insightextraction mechanism exist that can be customized to work for differentkind of entities and relations. Customizable summaries demand that anarticle may need to be summarized in different ways for different targetgroups, as their entities, concepts or relations of interest may bedifferent.

Information distillation from unstructured text data has always been acore research area for text analytics researchers. Information rich textdata in form of news is publicly available for consumption intoanalytics platform. However, with so many information source providerscome the curse of information overload which if not managed couldreverse the benefits of information availability.

Capturing the right information component from a pile of text is achallenging task. Moreover, information need of different users are alsodifferent. The same information may not be relevant for two differentusers. In the present disclosure, a system is provided that collects andprocesses text data, and delivers causal insights derived from text datato different users according to their interests.

Embodiments of the present disclosure provide system and method thatgenerate summaries such as around stock movements and their reasons frombusiness news articles, safety incidents, their probable causes, andpenalties from safety reports as well as possible risks to theenvironment caused by organizational activities as reported in News orany other web channels. Such examples of summaries shall not beconstrued as limiting the scope of the present disclosure. Morespecifically, system and method are provided by the present disclosurefor causal information extraction and a causal insight generationalgorithm for generating causal insight summary. Apart from detectingevent of importance, the system is responsible for providing insights asto why an event is relevant to a receiver.

News/Report data collected from web/internet is pre-processed/cleaned toobtain core text components along with all associated metadata likedate, source and author etc. The text data is then subjected to Namedentity extraction and sentiment analysis. Next, sentences mentioningcause-effects are identified and the cause-effect text segments/snippetcomprised within the sentences are extracted. In one instance, text datais taken as inputs and various impacting events present in it areidentified/detected. Customizable event indicator list acts as thepluggable component for this module with a generic algorithm that worksindependent of the domain of input indicator set.

The extracted information components and then passed to knowledge drivenreasoner where entities identified are assigned role labels with respectto their role in an identified event, alias of entities are resolved toa common global representation and identified entities are enriched withknowledge derived from external semantic knowledge bases.

Subsequently, scores are computed for the sentences in the input text byfactoring in the information components extracted as mentioned above. Aleast score optimal causal insight is generated next, as output. Lastly,user's interest in terms of events, entities, are pulled in and matchedagainst the generated insights to create and deliver personalizedinsights through email/push mobile notifications.

Referring now to the drawings, and more particularly to FIGS. 1 through3 , where similar reference characters denote corresponding featuresconsistently throughout the figures, there are shown preferredembodiments and these embodiments are described in the context of thefollowing exemplary system and/or method.

FIG. 1 depicts an exemplary system 100 for generating causal insightsummary, in accordance with an embodiment of the present disclosure. Inan embodiment, the system 100 includes one or more hardware processors104, communication interface device(s) or input/output (I/O)interface(s) 106 (also referred as interface(s)), and one or more datastorage devices or memory 102 operatively coupled to the one or morehardware processors 104. The one or more processors 104 may be one ormore software processing components and/or hardware processors. In anembodiment, the hardware processors can be implemented as one or moremicroprocessors, microcomputers, microcontrollers, digital signalprocessors, central processing units, state machines, logic circuitries,and/or any devices that manipulate signals based on operationalinstructions. Among other capabilities, the processor(s) is/areconfigured to fetch and execute computer-readable instructions stored inthe memory. In an embodiment, the system 100 can be implemented in avariety of computing systems, such as laptop computers, notebooks,hand-held devices (e.g., smartphones, tablet phones, mobilecommunication devices, and the like), workstations, mainframe computers,servers, a network cloud, and the like.

The I/O interface device(s) 106 can include a variety of software andhardware interfaces, for example, a web interface, a graphical userinterface, and the like and can facilitate multiple communicationswithin a wide variety of networks N/W and protocol types, includingwired networks, for example, LAN, cable, etc., and wireless networks,such as WLAN, cellular, or satellite. In an embodiment, the I/Ointerface device(s) can include one or more ports for connecting anumber of devices to one another or to another server.

The memory 102 may include any computer-readable medium known in the artincluding, for example, volatile memory, such as static random-accessmemory (SRAM) and dynamic-random access memory (DRAM), and/ornon-volatile memory, such as read only memory (ROM), erasableprogrammable ROM, flash memories, hard disks, optical disks, andmagnetic tapes. In an embodiment, a database 108 is comprised in thememory 102, wherein the database 108 comprises text description (e.g.,news, article, and the like) obtained from various sources (e.g., webportals, blogs, and the like). The database 108 further comprises one ormore named entities extracted from the pre-processed text description,one or more identified polarities of sentences comprised in thepre-processed text description, one or more cause effects sentences, andone or more causal relationship identified between text segments in theone or more cause effects sentences, role labels of each named entity,score for one or more sentences in the text description, causal insightsummary, and the like.

Further, the database 108 stores information pertaining tointerdependence of the one or more sentences, and (i) a cause, (ii) aneffect, and (iii) an associated causal relationship comprised in thetext segments of the text description obtained. Furthermore, thedatabase 108 comprises an overall polarity computed for the causalinsight summary, interest/preferences of the one or more target usersfor whom the causal insight summary is delivered/communicated. Thememory 102 further stores one or more techniques such as named entityrecognition technique(s), sentiment analysis technique, and the likewhich when executed by the system 100 perform the method describedherein. The memory 102 further comprises (or may further comprise)information pertaining to input(s)/output(s) of each step performed bythe systems and methods of the present disclosure. In other words,input(s) fed at each step and output(s) generated at each step arecomprised in the memory 102 and can be utilized in further processingand analysis.

FIG. 2 , with reference to FIG. 1 , depict an exemplary high level blockdiagram of the system 100 for generating causal insight summary, inaccordance with an embodiment of the present disclosure.

FIG. 3 , with reference to FIGS. 1-2 , depicts an exemplary flow chartillustrating a method generating causal insight summary, using thesystem 100 of FIG. 1 , in accordance with an embodiment of the presentdisclosure. In an embodiment, the system(s) 100 comprises one or moredata storage devices or the memory 102 operatively coupled to the one ormore hardware processors 104 and is configured to store instructions forexecution of steps of the method by the one or more processors 104. Thesteps of the method of the present disclosure will now be explained withreference to components of the system 100 of FIG. 1 , the block diagramof the system 100 depicted in FIG. 2 , and the flow diagram as depictedin FIG. 3 . In an embodiment, at step 202 of the present disclosure, theone or more hardware processors 104 obtain a text description from oneor more sources. The text description may be news, or an article, areport, and the like from say a news portal, a blog, an incidentreporting website, and the like. News data from the web is collectedthrough RSS (e.g., RDF Site Summary or Really Simple Syndication) feedsprovided by various news sources. A crawler module (refer FIG. 2 )aggregates the published news links from various RSS feeds and thencollects HTML data from the collected links. Other website data aregathered through crawlers. For instance, the system 100 receives thefollowing news article headline and along with other related informationas below:

Article 1: ABC Energy ordered to pay 500,000 USD after overchargingseveral thousands of customersThe above headline may be served as a link from an internet web portal,in one embodiment. The article may include text description of the newin detail and the language may be coarse in nature or may include othernoise from the web portal(s). Thus, to obtain a clean text description,the text description of the above news headline article ispre-processed. For the sake of brevity, the entire news article is notshown.

In an embodiment of the present disclosure, at step 204, the one or morehardware processors 104 pre-process the above text description to obtainpre-processed text description. In an embodiment, the system 100implements as known in the art pre-preprocessing technique(s) toeliminate/filter unwanted text from the news article/text description.Post pre-processing, the output serving as the pre-processed textdescription may include text as shown below:

“Title: ABC Energy to pay 500,000 USD for overcharging its customersURL: www.newsintoday.comPublication date: Wednesday Jul. 17, 2019, 8.30 AM USS1: ABC Energy is to pay 500,000 USD in fines and compensation afterovercharging around 8,000 customers.S2: The company, which was known as Utility Inc. until being bought andrenamed by a giant ABC Energy, overcharged on its default tariffs afterthe governments energy price cap was introduced in early 2019.S3: ABC's watchdog XYZ found that between the first quarter of the year2019 ABC Energy levied charges totaling 150,670.30 USD in excess of thecap.S4: In addition, the supplier will pay 150,000 USD to YYY consumerredress fund to help support vulnerable customers and 75,000 USD incompensation, equating to a total payment of 375,000 approximately.S5: The price cap for 8 million customers on poor value default tariffscame into force on 1 Jan. 2018.S6: ABC Energy is the first company to face enforcement action for capbreaching.S7: Other companies are understood to have cap breached but consumerdetriment was significantly smaller than that caused by ABC Energy, soXYZ did not take enforcement action.S8: ABC Energy will re-credit the accounts of affected customers withthe amount they were overcharged plus additional compensation.S9: Around 5,800 customer accounts were on tariffs that were notcompliant with the price cap, in the sense they were paying above thanthe cap level for their gas, electricity or both.S10: In addition to a refund each of these customers will receive anadditional 15 per fuel.S11: The remaining 2,300 customer accounts experienced a delay in theirenergy price being reduced under the price cap after they requested tochange to a cheaper means of paying for their energy.S12: This meant they were paying above the cap level for longer thannecessary.S13: They will each receive a refund plus an extra 4 per fuel.S14: In total the 10,000 customer accounts affected around 8,100customers.S15: XYZ said it had taken into account the fact that ABC Energy hadtaken steps to address its failings and to pay redress.S16: John Doe, chief executive of ABC Energy, apologized to allcustomers who were temporarily out of pocket.. . .S29: Purchasing Utility Inc in 2017 marked ABC's first step into retailgas and electricity supply as it looks to diversify away from its corefossil fuels business and take on the Big 4 energy suppliers.S30: Majority of the groups 400 bn annual revenues approximately stillcome from oil and gas.”The above exemplary text description, S1, S2, and so on up to S30reference correspond to number of sentences in the received textdescription/pre-processed text description.

In an embodiment of the present disclosure, at step 206, the one or morehardware processors 104 identify, by using a named entity recognitiontechnique, one or more named entities from the pre-processed textdescription. For instance, named entities such as Organization, Person,Location, Money value, etc. are recognized/identified/extracted from thetitle and content of each collected article using one or more naturallanguage processing libraries. In the present disclosure, the system 100implemented spaCy NLP technique comprised in the memory 102 and invokedfor execution. Spacy NLP API is natural language processing python API.For example, from the above pre-processed text description, some of thenamed entities extracted by the system 100 may include ABC Energy, JohnDoe, XYZ, Utility Inc., and the like. It is to be understood by a personhaving ordinary skill in the art or person skilled in the art thatexample of such NLP technique used for pre-processing of the textdescription shall not be construed as limiting the scope of the presentdisclosure.

In an embodiment of the present disclosure, at step 208, the one or morehardware processors 104 perform a sentiment analysis on thepre-processed text description to identify one or more polarities ofsentences comprised in the pre-processed text description. In anembodiment of the present disclosure, the system 100 obtained polarityof sentences, paragraphs and documents using a known in the artsentiment analysis library such as flairNLP comprised in the memory 102and invoked for execution. It is to be understood by a person havingordinary skill in the art or person skilled in the art that example ofsuch NLP technique used for identifying one or more polarities ofsentences comprised in the pre-processed text description shall not beconstrued as limiting the scope of the present disclosure. In thepresent disclosure, a few of the following exemplary polarities ofsentences identified are shown below in Table 1 along with thecorresponding score:

TABLE 1 Polarity/ Sentences Sentiment Score ABC Energy is to pay 500,000Negative −0.9987 USD in fines and compensation after overcharging around8,000 customers The company, which was known Negative −0.9833 as UtilityInc. until being bought and renamed by a giant ABC Energy, overchargedon its default tariffs after the governments energy price cap wasintroduced in early 2019. ABC's watchdog XYZ found that Negative −0.9975between the first quarter of the year 2019 ABC Energy levied chargestotaling 150,670.30 USD in excess of the cap. John Doe, chief executiveof ABC Negative −0.9941 Energy, apologized to all customers who weretemporarily out of pocket. Purchasing Utility Inc in 2017 Positive0.9953 marked ABC's first step into retail gas and electricity supply asit looks to diversify away from its core fossil fuels business and takeon the Big 4 energy suppliers. Majority of the groups 400 bn Positive0.7116 annual revenues approximately still come from oil and gas.

In an embodiment of the present disclosure, at step 210, the one or morehardware processors 104 extract one or more cause effects sentences inthe pre-processed text description and identify one or more causalrelationship between text segments in the one or more cause effectssentences. The one or more cause effects sentences correspond to one ormore impacting events. In other words, the one or more cause effectssentences are indicative of the impacting events. Cause effectssentences in text description (e.g., the pre-processed text description)are those that express causality between different elements mentioned inthe text description. Cause effects sentences (e.g., also referred ascausal sentences) contain one or more of the following an antecedent, aconsequence, and a causal connector. In the above pre-processed textdescription, some of the cause effects sentences extracted by the systemare shown below by way of examples:

-   -   1. ABC Energy is to pay 500,000 USD in fines and compensation        after overcharging around 8,000 customers.    -   2. In addition, the supplier will pay 150,000 USD to YYY        consumer redress fund to help support vulnerable customers and        75,000 USD in compensation, equating to a total payment of        375,000 approximately.    -   3. Other companies are understood to have cap breached but        consumer detriment was significantly smaller than that caused by        ABC Energy, so XYZ did not take enforcement action.    -   4 . . . .    -   5. Purchasing Utility Inc in 2017 marked ABC's first step into        retail gas and electricity supply as it looks to diversify away        from its core fossil fuels business and take on the Big 4 energy        suppliers.    -   6. Majority of the groups 400 bn annual revenues approximately        still come from oil and gas.

In the above extracted cause effect sentences, one or more causalrelationship between text segments in the one or more cause effectssentences are identified. For instance, in sentence 1—the cause orantecedent herein refers to ‘overcharging around 8,000 customers’, insentence 2—the cause or antecedent herein refers to ‘the supplier willpay 150,000 USD to XYZ consumer redress fund to help support vulnerablecustomers and 75,000 USD in compensation’ in sentence 3—the cause orantecedent herein refers to ‘ABC Energy’, . . . in sentence 5—the causeor antecedent herein refers to ‘looks to diversify away from its corefossil fuels business and take on the Big 4 energy suppliers’, and insentence 6—the cause or antecedent herein refers to ‘oil and gas’.

Similarly, consequence in the above cause effects sentence include suchas ‘ABC Energy is to pay 500,000 USD in fines and compensation’,‘payment’, ‘consumer detriment’, . . . ‘marked ABC's first step intoretail gas and electricity supply’, and ‘Majority of the groups 400 bnannual revenues’ respectively. Similarly, associated causal relationshipor causal connector include such as ‘after’ from sentence 1, ‘to’ fromsentence 2, ‘caused by’ from sentence 3, . . . ‘from’ from sentence 6,respectively.

Joint model for causal effect sentence classification and causalrelation extraction is performed based on Bidirectional EncoderRepresentations from Transformers (BERT)-based language model. Asmentioned earlier, the first task is to identify causal effectssentences within a text description. Causal effect sentenceclassification is modelled as a binary classification problem. Thepredicted label y1€ {0,1}, where 0 stands for a non-causal sentence and1 indicates that the sentence contains causal relations. The difficultyof the task is highlighted with a pair of sentences below that look verysimilar but belong to different classes. It is important that the entiresequence of words that comprise cause, effect or causal connectives in asentence are correctly labelled for better classification and furtherprocessing. The system 100 implements as known in the art BERT model(not shown in FIGS.), that generates contextual embedding for the inputtext, which is thereafter fed to a CNN-BiLSTM layer (not shown in FIGS.)followed by a fully connected layer (not shown in FIGS.) that jointlyperform the sentence classification and sequence labelling tasks. It wasobserved that the joint models of BERT model and CNN-BiLSTM layerexploit the dependencies between the two tasks and thereby improve theperformance over independent models. Below description on Fine-tuningthe BERT Language Model as implemented by the system 100 is discussed:

BERT uses a multi-layer bidirectional Transformer encoder architecturebased on the transformer model proposed by (Vaswani, 2017). It uses 12layers of transformer blocks, 768 hidden units, and 12 self-attentionheads. The model is pre-trained with two strategies on large-scaleunlabeled text-masked language model and next sentence prediction. Theinput representation is a concatenation of Word-Piece embeddings,positional embeddings, and the segment embedding. The pre-trained BERTmodel provides a powerful context-dependent sentence representation andcan be used for various target tasks through the fine-tuning procedure.Bio-BERT is another base model, that is specifically trained onBio-medical literature. This can also be fine-tuned with task-specificdata, if required.

Fine-tuning the pre-trained model with training data from differentdomains improves the performance of language processing tasks. Thepresent disclosure and the system 100 implemented Xavier initializationto ensure that the BERT fine-tuning process converges. Further, earlystopping of fine-tuning to 800 steps was set in order to preventover-fitting. A batch size 32, a maximum sequence length of 128, and alearning rate of 2*10-5 were used for fine-tuning this model.

Learning the Joint Model for Sentence Classification and SequenceLabelling Tasks is Described Below:

The sequence label prediction of a single word is dependent onpredictions for surrounding words. It has been shown that structuredprediction models such as cascaded CNN and LSTM models can significantlyimprove the sequence labelling performance. In (Zhou, 2015), it isstated that the performance of semantic role labelling improves byadding a conditional random field (CRF) layer along with a Bidirectionallong short-term memory (BiLSTM) encoder. In the present disclosure, thesystem and method investigated the efficacy (not shown in FIGS.) ofadding a CNN-BiLSTM layer for the combined tasks of sentenceclassification and sequence label prediction, on top of the BERT model.

For the causality classification and causal relation extraction task,the system 100 trained the CNN+BiLSTM layers using two loss functionsseparately. The system 100 used cross-entropy loss function to train theabove models end-to-end. Given a set of training data, x_(t), w_(t)^(i), y _(t) and w _(t) ^(i), where x_(t) is the t-th word sequence tobe predicted as causal or not-a causal text, w_(t) ^(i) is the i-th wordwithin the sentence to be predicted as Cause (C), effect (E), causalconnective (CN), and None (N), y _(t) and w _(t) ^(i) are the one-hotrepresentation of the ground-truth class labels for x_(t) and w_(t) ^(i)respectively and y^(i) and y_(n) ^(s) are the respective modelpredictions for both x_(t) and w_(t) ^(i). Thus, the causalityclassification is predicted as:

y ^(i)=softmax(W _(i) *h ₁ +b _(i))  (1)

wherein W_(i)—refers to weight matrix for a sentence, h₁—refers tohidden layer of the neural network, and b_(i)—refers to bias parameterof i^(th) sentence.

On the other hand, for the sequence labelling task, the final hiddenstates of the BERT-CNN-LSTM network of the other tokens, h₂, . . . ,h_(T), are fed into a softmax layer to classify over the sequencelabels. To make this procedure compatible with the Word-Piecetokenization, each tokenized input word is fed into a Word-Piecetokenizer and the hidden state corresponding to the first sub-token isused as input to the CNN-BiLSTM network and finally to a softmaxclassifier. The output of the model is represented as:

y _(n) ^(s)=softmax(W ^(s) *h _(n) +b _(s)),n€(1 . . . N)  (2)

W^(s)—refers to weight matrix of a word in the entire sentence,h_(n)—refers to n^(th) hidden layer in the neural network (where h_(n)is the hidden state corresponding to the first sub-token of word x_(n)),and b_(s)—refers to bias parameter of individual word in the entiresentence. Thus, the loss functions for the text classification (L1) andcausal relation extraction task (L2) are separately defined as:

L ₁(θ)=−Σ_(t=1) ^(N)Σ_(j=1) ^(J) q _(t) ^(i,j) log(q _(t) ^(i))  (3)

L ₂(θ)=−Σ_(t=1) ^(M)Σ_(k=1) ^(K) y _(t) ^(K) log(v _(t))  (4)

Where y_(t) is the vector representation of the predicted output of themodel for the input sentence x_(t). Similarly, q_(t) is the vectorrepresentation of the predicted output of the model for the input wordw_(t) ^(i). K and J are the number of class labels for each task. Themodel is fine-tuned end-to-end via minimizing the cross-entropy loss.The joint loss function using a linear combination of the loss functionsof the two tasks as:

L _(joint)(θ)=λ*L ₁(θ)+(1−λ)*I _((y) _(sentence) ₌₌₁₎ *L ₂(θ)  (5)

Where, λ controls the contribution of losses of the individual tasks inthe overall joint loss. I_((y) _(sentence) ₌₌₁₎ is an indicator functionwhich activates the causal relation labelling loss only when thecorresponding sentence classification label is 1, since back-propagaterelation labelling loss is not required when the corresponding sentenceclassification label is 0.

Referring to steps of FIG. 3 , at step 212 of the present disclosure,the one or more hardware processors 104 assign a role label to each ofthe one or more named entities. The role label corresponds to a role ofeach of the one or more entities in a corresponding event of the one ormore impacting events. It has been observed that a single document/textdescription may contain multiple different entities. Each entity has gota specific role in the overall semantics of the document. For example,if organization names as entities, then in the above example ofpre-processed text description, the entities include ABC Energy, XYZ,that are playing the role and hence these entities are role labeled.Identification of entity names from a given text document/descriptioncan be done using any off the shelf named entity recognizers (e.g., asknown in the art techniques). However, identification of semantic rolesassociated to each entity is a non-trivial task. Therefore, it isimportant to assign “role” labels to each entity based on their nature(Person/Organization/Money value etc.) etc.), semantic roles(Subject/and relative frequency scores. Key entities are thusdifferentiated from other entities. The entities of interests are neededto be provided to the system 100 wherein business rules and regularexpressions are implemented (not shown in FIGS.) to identify theentities of interest from unstructured text documents. For example, ifthe entity of interest is the of interest is the target or offendingorganization name, then the algorithm to detect such organization nameis depicted below:

Algorithm to Detect Target Organization Name from Unstructured Documentsfor Role Labeling:

In any document/text description, all the mentioned organizations maynot be of interest to the user or may not be relevant to the causalsummary. In order to identify the organizations/entities for pertinentto the text description, key organizations (Key_Org) and theorganization committing an offence (Offending_Org) herein referred asrole in the given scenario—role labelling is done by the followingsteps—

-   -   1. Organization NERs are first extracted using spacy. Let O be        the set of extracted organization in document D    -   2. ∀ ŏ ε O, f(Ŏ) is calculated as the frequency of occurrence of        ŏ in D.    -   3. ∀ ŏ ε O, ŏ ε Key_Org iff f(ŏ)>=2    -   4. For any organization ŏ in sentence S, ŏ ε Offending_Org iff        an Event is detected in S and ŏ ε Key_Org

Similarly, separate business rules and regular expressions are formedfor other type of entities of interest.

In the above pre-processed text description, the role label is assignedto ABC Energy in the sentences, for example: ABC Energy is to pay500,000 USD in fines and compensation after overcharging around 8,000customers, ABC Energy is the first company to face enforcement actionfor cap breaching, and the like. Similarly, the role label is assignedto XYZ, and ABC Energy in the sentences, for example: ABC's watchdog XYZfound that between the first quarter of the year 2019 ABC Energy leviedcharges totaling 150,670.30 USD in excess of the cap, XYZ said it hadtaken into account the fact that ABC Energy had taken steps to addressits failings and to pay redress, and the like.

In an embodiment of the present disclosure, at step 214, the one or morehardware processors, compute a score for one or more sentences in thetext description based on a presence of (i) the one or more identifiednamed entities based on the role label assigned, (ii) the one or moreidentified polarities, (iii) the one or more cause effects sentences,and (iv) the one or more impacting events. In one embodiment, sentencescores are generated on the basis of presence of information components,proximity, and interdependencies. Interdependence of the one or moresentences is based on the position of (i) each of the one or moreidentified named entities, (ii) the one or more identified polarities,and (iii) the one or more cause effects sentences in the pre-processedtext description. In other words, interdependence of the one or moresentences is based on the position of (i) each of the one or moreidentified named entities, (ii) the one or more identified polarities,and (iii) the one or more cause effects sentences comprised/present inthe pre-processed text description. The scoring mechanism as implementedby the system 100 assigns scores to sentences based on the role labelsthat are assigned to their content by the NLP technique(s) describedherein and using the outputs of each step described herein. Sentencesthat contain one or more information components like entities, phrases,incidents etc. are better candidates for inclusion in the insights.Causal sentences are also considered better candidates for insightgeneration. Interdependence is approximated by closeness of occurrenceof information components within text description. Thus, if a sentencecontains incidents along with entities or other elements of interest orother elements of interest and is also causal, it can be a very goodcandidate for insight for insight generation. The scores computed foreach of the above components is shown by way of examples in below Table2:

TABLE 2 Selection Extraction presence in CAUSE_ optimal ORG_ MONEY_ LOC_PENALTY_ EFFECT_ VIOLATION_ INCIDENT_ insight Sentences Present PresentPresent Present Present Present Present Selected S1 1 0 0 1 1 0 0 1 S2 10 0 0 0 0 0 0 . . . . . . . . . . . . . . . . . . . . . . . . . . . S9 00 0 0 1 1 0 1

For the sake of brevity, interdependencies is shown in above table 2 forsentences S1, S2 and S9 and such examples shall not be construed aslimiting the scope of the present disclosure.

Below Table 3 depicts inter and intra scores based on proximity andconfidence between sentences in the text description. Table 3 alsodepicts a final score for each sentence of the text description:

TABLE 3 bucket_ bucket_ bucket_ bucket_ bucket_ bucket_ bucket_ bucket_Final Sentences 01 02 10 11 12 20 21 22 score S1 14.28571429 10 1917.28571429 15 0 0 0 38.08774601 S2 6.666666667 5 4 3.166666667 2.5 0 00 16.61529553 S3 6.7 5 4 3.2 2.5 0 0 0 16.61703243 S4 2.25 0 8 7.5 5 0 00 15.76151729 S5 0 0 5.45785 6.124516667 3.45785 0 0 0 12.67639909 . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . S30 0 011.88916667 13.5225 11.1225 0 0 0 19.46935816

For the sake of brevity, interdependencies is shown in above Table 2 forS1, till S5 and S30 and such examples shall not be construed as limitingthe scope of the present disclosure.

Sentence that contains at least one value is retained for scoring, whilethe remaining ones are assigned a score of 0. The final score assignedto a sentence depends on four factors, which are explained below:

1. Sentence-Title Similarity (TitleSim(S_(i))): Cosine similarity ofsentence S_(i) with title T is calculated using the sentence embeddingsgenerated by Infersent. Since cosine similarity ε[0,1], the system 100boosts it with factor of ω (>=1) to ensure consistency among valuesranges.

-   -   2. Confidence score from sentence type: Once all the entities        are extracted, we assign a Confidence score to the extractions        on the basis of how relevant they are to the domain using the        following rules:        -   Confidence score is increased by reward R when—            -   a. For any extracted value v of slot S found in sentence                S_(i), If v is present in the title            -   b. For any extracted value v of slot S found in sentence                S_(i), If v is present in cause-conn-effect excerpt            -   c. For any extracted organization ŏ, if ŏ ε Key_Org or ŏ                ε Offending_Org            -   d. If a sentence S_(i) has cause-conn-effect                extractions, a reward of 2*R is given when all 3 factors                are extracted and R if 2 out of the 3 aspects are                extracted    -   Confidence score is given a penalty P when—        -   a. POS(S_(i)) is a pronoun, where POS is the part of speech            of the subject of the sentence S_(i).            -   For example: in the sentence—‘It has donated $2 m to the                WHO fund so far.’, the subject of the sentence, ‘It’, is                not helping the reader comprehend the context of the                sentence. Therefore, it is not very informative.    -   Here, R and P values as (ω+1)/4    -   3. Intra-bucket score: Sentences containing values for certain        slots also gain for being in proximity of other sentences        containing values in the same bucket. As a corollary, between        two sentences that contain values for the same slot, the one        that contains additional values for other slots belonging to the        same bucket scores higher. This is referred to as intra-bucket        score of a sentence.    -   4. Inter-bucket score: Sentences also gain some weight from        being in proximity to other sentences that contain values for        slots from other buckets. The inter bucket proximity ensures        that the overall context of all the findings remains consistent.

Proximity between two sentences S_(i) and S_(j), is computed as aninverse function of the distance between the sentences in the document.

Proximity(S _(i) ,S _(j))=(1)/(1+Distance((S _(i) ,S _(j)))

Where distance(S_(i), S_(j))=abs (position(S_(i))−position(S_(j))),Where position(S_(i)) indicates sentence number of S_(i).Let V={v₁, v₂, . . . v_(m)} be the set of values required for insightgeneration. Then the scores for the sentence S_(i) having a value v_(k)is expressed as follows:

${{Intra\_ Bucket}{\_ score}} = {\sum\limits_{k}\left( {{{Confidence}{}\left( v_{k} \right)} + {\sum\limits_{p}\left( {\max\left( {{Proximity}\left( {S_{i},S_{j}} \right)} \right)} \right)}} \right)}$

∀v_(k), v_(p) ε V, such that bucket(v_(p))=bucket(v_(k)), ∀ j such thatS_(i) is the closest sentence that contains a value for a slot thatbelongs to the same bucket, including itself.

$\left. {{{Intra\_ Bucket}{\_ score}} = {{\sum\limits_{k}\left( \left. {Confidence}(v_{k} \right) \right)} + {\sum\limits_{p}\left( {\max\left( {{Proximity}\left( {S_{i},S_{j}} \right)} \right)} \right)}}} \right)$

∀v_(k), v_(p) ε V, such that bucket(v_(p))·bucket(v_(k)), ∀ j such thatS_(j) is the closest sentence that contains a value for a slot thatbelongs to the different bucket.Score(S_(i)) is now computed as:

Score(S _(i))=α(Intra Bucket Score Score(S _(i)))+(1+α)(Inter BucketScore Score(S _(i)))+ω*TitleSim(S _(i)) such that α>=0.5

The score is then normalized such that Score(S₁) ε[0,1].

Referring to steps of FIG. 3 , at step 216 of the present disclosure,the one or more hardware processors 104 generate processors, a causalinsight summary based on the computed score and the presence of (i) theone or more identified named entities based on the role label assigned,(ii) the one or more identified polarities, (iii) the one or more causeeffects sentences, and (iv) the one or more impacting events in thepre-processed text description. In the present disclosure, the sentencesS1 and S9 were selected based on the observations made in Table 2 andTable 3, therefore the system 100 generated a causal insight summaryaccounting for sentences S1 and S9 wherein the causal insight summary isdepicted by way example below:

Generated Causal Insight Summary:

“ABC Energy is to pay 500,000 USD in fines and compensation afterovercharging around 8,000 customers. Around 5,800 customer accounts wereon tariffs that were not compliant with the price cap, in the sense theywere paying above than the cap level for their gas, electricity orboth.”

In the above causal insight summary generated by the system 100, theorganization/named entity is ABC Energy, penalty is pay 500,000 USD,violation is paying above than the cap level for their gas, electricityor both, cause is overcharging around 8,000 customers. Around 5,800customer accounts were on tariffs that were and with the price cap,cause-effect connector (or cause connector) are after and with the pricecap and effect is ABC Energy is to pay 500,000 USD in fines andcompensation.

More specifically, in the above pre-processed text description, keyorganization extracted is ABC Energy, ESG Controversy:Fines→Environmental compliance→Environment for causal insight summarygeneration. The above generation of causal insight summary may be betterunderstood by way of following description:

The objective is now to use the above scores to identify the minimal setof sentences that can form an insight (e.g., causal insight summary).Suppose there are ‘m’ slots divided into different buckets.

Let S={S₁, S₂, . . . S_(n)} be the set of sentences which have anon-zero scores after computing scoring.

The following optimization technique/algorithm finds the minimal set ofsentences that contain all the ‘m’ values, if present.

Let VS(i,j)=1, if value v_(i) is found in sentence S_(j)

-   -   0, otherwise        Let x(i)=1, if value S_(i) is found in optimal insight    -   0, otherwise        Then the objection function for the optimization problem is        expressed as:

MaximizeΣ_(i)(x(i)*(Score(S _(i))−1))

Subject to constraints:

Σ_(i)(VS(i,j)x(i))>=1∀v _(j)foundinD  (C1—constraint 1)

Σ_(i)(x(i))<=|V|  (C2—constraint 2)

Σ_(i)(x(i))>=1  (C3—constraint 3)

The value (−1) is added in the objective function to ensure that minimumnumber of sentences are finally selected. The constraint in equation C1ensures that at least 1 sentence is picked to cover each slot value,provided that value is reported by the document/textdescription/pre-processed text description. Finally, equations C2 and C3enforce that at least 1 sentence is selected from the document andmaximum number of sentences selected are no more than the type of valuesrequired to address the user given query. This is solved using IntegerLinear programming, in one embodiment of the present disclosure. Suchtechnique of solving the optimization problem shall not be construed aslimiting the scope of the present disclosure.

Once the causal insight summary is generated by the system 100, the oneor more hardware processors 104 computing an overall polarity of thegenerated causal insight summary. Below Table 4 depicts the generatedcausal insight summary along with the overall polarity score.

TABLE 4 Overall polarity (or Insight Article/News Generated causalTarget sentiment Headlines insight summary Organization value) ABCEnergy to ABC Energy is to ABC Energy −1.9984 pay 500,000 pay 500,000USD USD for in fines and overcharging compensation its customers afterovercharging around 8,000 customers. Around 5,800 customer accounts wereon tariffs that were not compliant with the price cap, in the sense theywere paying above than the cap level for their gas, electricity or both.

The overall polarity/sentiment is based on a summation over selectedsentences to report final insight sentiment (or causal insight summary),in one embodiment of the present disclosure. Computation of overallpolarity score computation as described and shown above is one of anapproach and such approach shall not be construed as limiting the scopeof the present disclosure.

Once the generated causal insight summary, the one or more hardwareprocessors 104 communicate/deliver at least a portion of the generatedcausal insight summary to one or more target users (e.g., target devicessuch as laptop, mobile communication device/devices via tickerinformation or tweets) based on an interest of the one or more targetusers for at least one of (i) one or more specific named entities, and(ii) the one or more impacting events. This may be referred ascustomized/personalized delivery of causal insight summary to targetusers/end users. For customized insight generation, a set of domainrelevant entities and aspects that are of interest to the end user areobtained. While looking for summaries or insights about organizationalviolations the following categories can be considered: 1. OrganizationNER, 2. Cause, Conn Effect, etc., 3. Events 4. Location NER, 5. MoneyNER, and the like.

These categories are referred to as slots. Slots can further be bucketedtogether to ensure meaningful information extraction. Bucketing is donesuch that the factors that are required to occur close to each other ina text belong in the same bucket. It can be interpreted as a contextprovider for the information components to ensure that randomlyoccurring strings or values of a certain type are not accepted justbecause of the presence of a slot instance. For example, a sentence ‘Itsaid it had paid more than $ 2 bn in tax since 2000’, a money valuealone is not informative to the user, whereas when an organization isalso associated with it like XYZMobile said it had also paid $440 m ayear for the past 14 years”, the sentence becomes much more relevant.Hence, Organization and Money NER can be placed in the same bucket.

Organizations/Users can express their requirements in the form ofinformation components that are of interest to them and how the variouscomponents are interdependent or correlated. This can be specified usingthe bucket arrangements in the system 100. Different bucket arrangementsenable the system to generate customized insights according to userrequirements. Following are various scenarios forcustomized/personalized causal insight summary delivery.

Scenario 1: Obtain Insights about Organizational Violations andIncidents

Bucket arrangements can be chosen such that events reporting certainviolation/incident and the penalty imposed can be selected for theinsight. The insights generated using this arrangement presents the userwith the target organization, the incident, and the resultant violationalong with the associated penalty if any. In an embodiment, thecustomized causal insight summary may be same as shown in step 216 andis illustrated below:

Customized/personalized causal insight summary to a target user (e.g.,say user 1)ABC Energy to pay 500,000 USD for overcharging its customers“ABC Energy is to pay 500,000 USD in fines and compensation afterovercharging around 8,000 customers. Around 5,800 customer accounts wereon tariffs that were not compliant with the price cap, in the sense theywere paying above than the cap level for their gas, electricity orboth.”Scenario 2: Obtain Insights from Business News to Support PersonalizedWealth Advisory

Relevant financial events and monetary extraction can be considered forusers looking for investment opportunities and building their portfolioThe bucket arrangement given below can be considered to present theorganizations involved in a certain financial event. This can help themin better decision making.

Customized/personalized causal insight summary to another target user(e.g., say user 2)IT majors beat street estimates in Q4—how should one play thesector/segment?“Deal wins were very strong and impactful during the quarter with ABCDTextile Company reporting a record deal wins of $5 billion while XYZTTextile company reported new deal wins of $3.9 billion, Marc posted thenumbers. We believe that investors should remain invested in ITcompanies given their breadth of offerings especially in digitaltechnologies which will allow them to win large deals.

Relevant Financial Event: Economic Performance.”

It may be noted that such incidents as described above in the articlesmay be reported by other news sources and reports. Since, the sameincident is reported in an article using different verbalrepresentations, the k means clustering algorithm (Likas et al.,) isimplemented by the system 100 to group all these instances together. Theincidents are represented as vectors and clustered using cosinesimilarity as the distance function. For example, two fragments “failureto de energize plant equipment” and “failing to lock out energy sourcesrepresent the same incident semantic ally. The k means clusteringalgorithm as implemented by the system 100 groups these two eventstogether. The implementation of the same is described below for betterunderstanding of the embodiments discussed herein.

It is observed that a number of extracted safety related hazardousissues shows a high degree of semantic similarity. For example, “failureto de energize plant equipment” and “failing to lock out energy sources”represents the same semantic sense. Therefore, these events are groupedinto cluster(s). Accordingly, similar incidents and violations aredetermined by executing an algorithm such as incident resolution. Thealgorithm follows the following steps: a) first identify the wordembeddings of each constituent word of an event using GloVerepresentation (Pennington, 2014) and then phrase embeddings are createdby computing a tensor product between the individual word andembeddings. For example, given two safety related issues C₁=w₁, w₂, w₁and C₂=w₁ ^(i), w₂ ^(i), . . . , w_(j) ^(i) where w₁, w₂, . . . , w_(k)and w₁ ^(i), w₂ ^(i), . . . , w_(k) ^(i) are the constituent wordembeddings of events C₁ and C₂ such that i≠j, the phrase embeddingP_((w) ₁ _(,w) ₂ ₎ is created by computing the tensor product of eachadjacent word embedding pairs. This is represented as P(w₁, w₂)=w₁⊗w₂.Similar word and phrase embeddings are constructed for safety issue. Thesimilarity is computed as

${S\left( {C_{1},C_{2}} \right)} = \frac{s^{\prime} + s^{''}}{N_{1} + N_{2}}$

where N₁ and N₂ are the cardinalities of C₁ and C₂ respectively. S′ andS″ are computed as:

S′=Σ _(∀w) _(i) _(εC) ₁ S _(w) _(i) ,S″=Σ _(∀p) ₁ _(εC) _(i) S _(p) _(i)

Where

S_(w_(i)) = max_(∀w_(j)^(i)ϵC₂)(Sim(w_(i), w_(j)^(i))andS_(p_(i)) = max_(∀p_(j)^(i)ϵC₂)(Sim(p_(i), p_(j)^(i)).

Again p and p′ are the individual phrase embeddings in sentence andrespectively. Sim(x,y) is the cosine similarity between the twoword-vectors w_(x) and w_(y). Based on the similarity score, a k meansclustering is performed to form clusters of similar causal events.“Average silhouette” method was used to identify number of clusters k.In the present disclosure, during the experiments conducted, the system100 obtained the value of k as 21. The name of the cluster was chosenfrom the most common noun chunks present in the cluster. For example,incidents pertaining to falling of workers during construction sites arerepresented by the term “Fall Hazards”. Similarly, incidents due to therelease of hazardous chemicals are represented by “Chemical Hazards”.

Conventionally, methods included for summarization included humanintervention both in terms of interpretation of numerical data andsummarization of text data from unstructured texts. Further, otherexisting methods involved use of linguistic rules identify causes andeffect. However, linguistic rules are known to break down very easily.Further, some other conventional method used a knowledge base along withlinguistic patterns to identify cause-effect. This system needsincidents to be defined in the knowledge base to recognize them ascause-effect in text. Since these are rules based, they may be prone toerror.

Embodiments of the present disclosure address the technical problems ofgenerating causal insight summaries to target users/end users whereinsystem and method are provided by the present disclosure that (i) curateheterogeneous streams of textual data, (ii) extract various elementssuch as entities, incidents, sentiments, causal relations, and othersfrom each piece of content, (iii) link relevant content across sources,(iv) generate optimal, self-explanatory insights focused around elementsof interest that are customizable, wherein the generated causalsummaries are kept brief in nature to avoid information overload, (v)mechanism to deliver the insights as alerts in a personalized way byobtaining personalization data of users (user preferences, professionalroles and responsibilities, behavior, investments, intent, etc.) fromenterprise database, through different media, etc. The personalizationuses business data shared by business systems, if any, as well as userprofile data gathered by the engine itself, in one embodiment of thepresent disclosure.

As mentioned above, the system extracts entities, and causal informationinvolving them automatically from text documents, generates domaindriven customized insights enhanced with the necessary data and metadata and further uses these in conjunction with the enterprise data todeliver the insights in a personalized form. Implementation of thesystem 100 is a non-trivial task as it requires both technologyexpertise as well as system-level understanding from the userperspective. Extraction of targeted information and linking them to getan intelligent inference mechanism for e-personalized insight generationsystem is efficiently achieved by the present disclosure.

The written description describes the subject matter herein to enableany person skilled in the art to make and use the embodiments. The scopeof the subject matter embodiments is defined by the claims and mayinclude other modifications that occur to those skilled in the art. Suchother modifications are intended to be within the scope of the claims ifthey have similar elements that do not differ from the literal languageof the claims or if they include equivalent elements with insubstantialdifferences from the literal language of the claims.

It is to be understood that the scope of the protection is extended tosuch a program and in addition to a computer-readable means having amessage therein; such computer-readable storage means containprogram-code means for implementation of one or more steps of themethod, when the program runs on a server or mobile device or anysuitable programmable device. The hardware device can be any kind ofdevice which can be programmed including e.g., any kind of computer likea server or a personal computer, or the like, or any combinationthereof. The device may also include means which could be e.g., hardwaremeans like e.g., an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), or a combination of hardware andsoftware means, e.g., an ASIC and an FPGA, or at least onemicroprocessor and at least one memory with software processingcomponents located therein. Thus, the means can include both hardwaremeans and software means. The method embodiments described herein couldbe implemented in hardware and software. The device may also includesoftware means. Alternatively, the embodiments may be implemented ondifferent hardware devices, e.g., using a plurality of CPUs.

The embodiments herein can comprise hardware and software elements. Theembodiments that are implemented in software include but are not limitedto, firmware, resident software, microcode, etc. The functions performedby various components described herein may be implemented in othercomponents or combinations of other components. For the purposes of thisdescription, a computer-usable or computer readable medium can be anyapparatus that can comprise, store, communicate, propagate, or transportthe program for use by or in connection with the instruction executionsystem, apparatus, or device.

The illustrated steps are set out to explain the exemplary embodimentsshown, and it should be anticipated that ongoing technologicaldevelopment will change the manner in which particular functions areperformed. These examples are presented herein for purposes ofillustration, and not limitation. Further, the boundaries of thefunctional building blocks have been arbitrarily defined herein for theconvenience of the description. Alternative boundaries can be defined solong as the specified functions and relationships thereof areappropriately performed. Alternatives (including equivalents,extensions, variations, deviations, etc., of those described herein)will be apparent to persons skilled in the relevant art(s) based on theteachings contained herein. Such alternatives fall within the scope ofthe disclosed embodiments. Also, the words “comprising,” “having,”“containing,” and “including,” and other similar forms are intended tobe equivalent in meaning and be open ended in that an item or itemsfollowing any one of these words is not meant to be an exhaustivelisting of such item or items, or meant to be limited to only the listeditem or items. It must also be noted that as used herein and in theappended claims, the singular forms “a,” “an,” and “the” include pluralreferences unless the context clearly dictates otherwise.

Furthermore, one or more computer-readable storage media may be utilizedin implementing embodiments consistent with the present disclosure. Acomputer-readable storage medium refers to any type of physical memoryon which information or data readable by a processor may be stored.Thus, a computer-readable storage medium may store instructions forexecution by one or more processors, including instructions for causingthe processor(s) to perform steps or stages consistent with theembodiments described herein. The term “computer-readable medium” shouldbe understood to include tangible items and exclude carrier waves andtransient signals, i.e., be non-transitory. Examples include randomaccess memory (RAM), read-only memory (ROM), volatile memory,nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, andany other known physical storage media.

It is intended that the disclosure and examples be considered asexemplary only, with a true scope and spirit of disclosed embodimentsbeing indicated by the following claims.

What is claimed is:
 1. A processor-implemented method, comprising:obtaining, via one or more hardware processors, a text description fromone or more sources; pre-processing, via the one or more hardwareprocessors, the text description to obtain pre-processed textdescription; identifying, by using a named entity recognition techniquevia the one or more hardware processors, one or more named entities fromthe pre-processed text description; performing a sentiment analysis, viathe one or more hardware processors, on the pre-processed textdescription to identify one or more polarities of sentences comprised inthe pre-processed text description; extracting, via the one or morehardware processors, one or more cause effects sentences in thepre-processed text description and identifying one or more causalrelationship between text segments in the one or more cause effectssentences, wherein the one or more cause effects sentences correspond toone or more impacting events; assigning, via the one or more hardwareprocessors, a role label to each of the one or more named entities,wherein the role label corresponds to a role of each of the one or moreentities in a corresponding event of the one or more impacting events;computing, via the one or more hardware processors, a score for one ormore sentences in the text description based on a presence of (i) theone or more identified named entities based on the role label assigned,(ii) the one or more identified polarities, (iii) the one or more causeeffects sentences, and (iv) the one or more impacting events; andgenerating, via the one or more hardware processors, a causal insightsummary based on the computed score.
 2. The processor implemented methodof claim 1, wherein each of the one or more cause effects sentencescomprises an antecedent, a consequence, and a causal connector.
 3. Theprocessor implemented method of claim 1, wherein interdependence of theone or more sentences is based on the position of (i) each of the one ormore identified named entities, (ii) the one or more identifiedpolarities, and (iii) the one or more cause effects sentences in thepre-processed text description.
 4. The processor implemented method ofclaim 1, wherein the text segments comprise at least one of (i) a cause,(ii) an effect, and (iii) an associated causal relationship.
 5. Theprocessor implemented method of claim 1, further comprising computing anoverall polarity of the generated causal insight summary.
 6. Theprocessor implemented method of claim 1, further comprisingcommunicating at least a portion of the generated causal insight summaryto one or more target users based on an interest of the one or moretarget users for at least one of (i) one or more specific namedentities, and (ii) the one or more impacting events.
 7. A system,comprising: a memory storing instructions; one or more communicationinterfaces; and one or more hardware processors coupled to the memoryvia the one or more communication interfaces, wherein the one or morehardware processors are configured by the instructions to: obtain a textdescription from one or more sources; pre-process the text descriptionto obtain pre-processed text description; identify, by using a namedentity recognition technique, one or more named entities from thepre-processed text description; perform a sentiment analysis on thepre-processed text description to identify one or more polarities ofsentences comprised in the pre-processed text description; extract oneor more cause effects sentences in the pre-processed text descriptionand identify one or more causal relationship between text segments inthe one or more cause effects sentences, wherein the one or more causeeffects sentences correspond to one or more impacting events; assign arole label to each of the one or more named entities, wherein the rolelabel corresponds to a role of each of the one or more entities in acorresponding event of the one or more impacting events; compute a scorefor one or more sentences in the text description based on a presence of(i) the one or more identified named entities based on the role labelassigned, (ii) the one or more identified polarities, (iii) the one ormore cause effects sentences, and (iv) the one or more impacting events;and generate a causal insight summary based on the computed score. 8.The system of claim 7, wherein each of the one or more cause effectssentences comprises at least one of an antecedent, a consequence, and acausal connector.
 9. The system of claim 7, wherein interdependence ofthe one or more sentences is based on the position of (i) each of theone or more identified named entities, (ii) the one or more identifiedpolarities, and (iii) the one or more cause effects sentences in thepre-processed text description.
 10. The system of claim 7, wherein thetext segments comprise at least one of (i) a cause, (ii) an effect, and(iii) an associated causal relationship.
 11. The system of claim 7,wherein the one or more hardware processors are configured by theinstructions to compute an overall polarity of the generated causalinsight summary.
 12. The system of claim 7, wherein the one or morehardware processors are configured by the instructions to communicate atleast a portion of the generated causal insight summary to one or moretarget users based on an interest of the one or more target users for atleast one of (i) one or more specific named entities, and (ii) the oneor more impacting events.
 13. One or more non-transitorymachine-readable information storage mediums comprising one or moreinstructions which when executed by one or more hardware processorscause: obtaining a text description from one or more sources;pre-processing the text description to obtain pre-processed textdescription; identifying, by using a named entity recognition technique,one or more named entities from the pre-processed text description;performing a sentiment analysis on the pre-processed text description toidentify one or more polarities of sentences comprised in thepre-processed text description; extracting one or more cause effectssentences in the pre-processed text description and identifying one ormore causal relationship between text segments in the one or more causeeffects sentences, wherein the one or more cause effects sentencescorrespond to one or more impacting events; assigning a role label toeach of the one or more named entities, wherein the role labelcorresponds to a role of each of the one or more entities in acorresponding event of the one or more impacting events; computing ascore for one or more sentences in the text description based on apresence of (i) the one or more identified named entities based on therole label assigned, (ii) the one or more identified polarities, (iii)the one or more cause effects sentences, and (iv) the one or moreimpacting events; and generating a causal insight summary based on thecomputed score.
 14. The one or more non-transitory machine-readableinformation storage mediums of claim 13, wherein each of the one or morecause effects sentences comprises an antecedent, a consequence, and acausal connector.
 15. The one or more non-transitory machine-readableinformation storage mediums of claim 13, wherein interdependence of theone or more sentences is based on the position of (i) each of the one ormore identified named entities, (ii) the one or more identifiedpolarities, and (iii) the one or more cause effects sentences in thepre-processed text description.
 16. The one or more non-transitorymachine-readable information storage mediums of claim 13, wherein thetext segments comprise at least one of (i) a cause, (ii) an effect, and(iii) an associated causal relationship.
 17. The one or morenon-transitory machine-readable information storage mediums of claim 13,wherein the one or more instructions which when executed by the one ormore hardware processors further cause computing an overall polarity ofthe generated causal insight summary.
 18. The one or more non-transitorymachine-readable information storage mediums of claim 13, wherein theone or more instructions which when executed by the one or more hardwareprocessors further cause communicating at least a portion of thegenerated causal insight summary to one or more target users based on aninterest of the one or more target users for at least one of (i) one ormore specific named entities, and (ii) the one or more impacting events.